Abstract
The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73%, which is really encouraging for further refining our approach.
Original language | English |
---|---|
Title of host publication | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Publisher | ISPRS |
Pages | 321-325 |
Volume | XLII-2/W7 |
DOIs | |
Publication status | Published - 2017 |
Event | ISPRS Geospatial Week 2017 - Wuhan, China Duration: 18 Sept 2017 → 22 Sept 2017 http://www.isprs.org/documents/geoweek.aspx |
Conference
Conference | ISPRS Geospatial Week 2017 |
---|---|
Country/Territory | China |
City | Wuhan |
Period | 18/09/17 → 22/09/17 |
Internet address |
Keywords
- Classification
- Feature extraction
- Mobile laser scanning
- Point clouds
- Urban area
- Vertical objects